72 lines
1.4 KiB
Python
72 lines
1.4 KiB
Python
from gtsam import *
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import numpy as np
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r = Rot3()
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print(r)
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print(r.pitch())
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r2 = Rot3()
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r3 = r.compose(r2)
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print("r3 pitch:", r3.pitch())
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v = np.array([.1, .1, .1])
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print("v = ", v)
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r4 = r3.retract(v)
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print("r4 pitch:", r4.pitch())
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r4.print_(b'r4: ')
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r3.print_(b"r3: ")
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v = r3.localCoordinates(r4)
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print("localCoordinates:", v)
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Rmat = np.array([
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[0.990074, -0.0942928, 0.104218],
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[0.104218, 0.990074, -0.0942928],
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[-0.0942928, 0.104218, 0.990074]
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])
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r5 = Rot3(R=Rmat)
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r5.print_(b"r5: ")
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l = Rot3.Logmap(r5)
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print("l = ", l)
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noise = noiseModel_Gaussian.Covariance(Rmat)
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noise.print_(b"noise:")
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D = np.array([1.,2.,3.])
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diag = noiseModel_Diagonal.Variances(D)
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print("diag:", diag)
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diag.print_(b"diag:")
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print("diag R:", diag.R())
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p = Point3()
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p.print_("p:")
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factor = BetweenFactorPoint3(1,2,p, noise)
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factor.print_(b"factor:")
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vv = VectorValues()
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vv.print_(b"vv:")
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vv.insert(1, np.array([1.,2.,3.]))
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vv.insert(2, np.array([3.,4.]))
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vv.insert(3, np.array([5.,6.,7.,8.]))
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vv.print_(b"vv:")
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vv2 = VectorValues(vv)
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vv2.insert(4, np.array([4.,2.,1]))
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vv2.print_(b"vv2:")
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vv.print_(b"vv:")
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vv.insert(4, np.array([1.,2.,4.]))
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vv.print_(b"vv:")
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vv3 = vv.add(vv2)
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vv3.print_(b"vv3:")
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values = Values()
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values.insertPoint3(1, Point3())
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values.insertRot3(2, Rot3())
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values.print_(b"values:")
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factor = PriorFactorVector(1, np.array([1.,2.,3.]), diag)
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print "Prior factor vector: ", factor
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